Collaborative filtering is an effective recommendation approach in which the preference of a
user on an item is predicted based on the preferences of other users with similar interests. A …

Collaborative filtering techniques are a common approach for building recommendations,
and have been widely applied in real recommender systems. However, collaborative …

Collaborative filtering is a fundamental building block in many recommender systems. While
most of the existing collaborative filtering methods focus on explicit, multi-class settings (eg …

The task of collaborative filtering is to predict the preferences of an active user for unseen
items given preferences of other users. These preferences are typically expressed as …

Collaborative filtering (CF), one of the most successful recommendation approaches,
continues to attract interest in both academia and industry. However, one key issue limiting …

Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender
systems, especially for new users and items. We observe that, while our target data are …

Matrix factorization (MF) has been demonstrated to be one of the most competitive
techniques for collaborative filtering. However, state-of-the-art MFs do not consider …

Collaborative filtering is a fundamental technique in recommender systems, for which
memory-based and matrix-factorization-based collaborative filtering are the two types of …

One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender
system settings. Typical characteristics include: Only positive examples can be observed …

Collaborative filtering aims at predicting a test user's ratings for new items by integrating
other like-minded users' rating information. The key assumption is that users sharing the …